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Modeling the spread of COVID-19 in spatio-temporal context
S H Sathish Indika1, Norou Diawara2, Hueiwang Anna Jeng3
1Department of Mathematics, Virginia Peninsula Community College, Hampton, VA 23666, USA.
Mathematical Biosciences and Engineering : MBE
|June 16, 2023
Summary
This study analyzes COVID-19 case trends across Virginia counties using advanced statistical models. It reveals spatial and temporal patterns in disease spread, offering insights for public health decision-making.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- COVID-19 has presented significant public health challenges globally and locally.
- Understanding the spatial and temporal dynamics of COVID-19 spread is crucial for effective control strategies.
- Virginia's Department of Public Health provides county-level data crucial for localized analysis.
Purpose of the Study:
- To analyze changes and trends in total COVID-19 cases across Virginia's 93 counties.
- To compare the relative spread and temporal evolution of COVID-19 incidence rates among counties.
- To provide a methodological template for similar epidemiological studies.
Main Methods:
- Bayesian conditional autoregressive framework utilizing Markov Chain Monte Carlo (MCMC) methods.
- Application of Moran spatial correlation and time series modeling techniques.
- Analysis of spatial and temporal counts of total COVID-19 cases using county-level data.
Main Results:
- Identified significant differences in the relative spread of COVID-19 across Virginia counties.
- Characterized the temporal evolution of COVID-19 case incidence rates over time.
- Demonstrated the utility of spatial-temporal modeling for understanding disease dynamics.
Conclusions:
- The study provides valuable insights into the spatial and temporal patterns of COVID-19 in Virginia.
- The employed statistical framework can serve as a model for analyzing infectious disease spread in other regions.
- Data-driven insights are essential for informing public health interventions and policy.
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